dbt-labs · runleonarun · Dec 4, 2024 · Nov 13, 2024 · Dec 3, 2024 · Dec 4, 2024
@@ -12,13 +12,17 @@ date: 2021-11-29
 is_featured: true
 ---
 
+import Latest from '/snippets/_release-stages-from-versionless.md'
+
+<Latest/>
+
 :::tip February 2024 Update
 
 It's been a few years since dbt-core turned 1.0! Since then, we've committed to releasing zero breaking changes whenever possible and it's become much easier to upgrade dbt Core versions.
 
 In 2024, we're taking this promise further by:
 - Stabilizing interfaces for everyone — adapter maintainers, metadata consumers, and (of course) people writing dbt code everywhere — as discussed in [our November 2023 roadmap update](https://github.com/dbt-labs/dbt-core/blob/main/docs/roadmap/2023-11-dbt-tng.md).
-- Introducing **Versionless** in dbt Cloud. No more manual upgrades and no more need for _a second sandbox project_ just to try out new features in development. For more details, refer to [Upgrade Core version in Cloud](/docs/dbt-versions/upgrade-dbt-version-in-cloud).
+- Introducing **Latest** release track in dbt Cloud. No more manual upgrades and no need for _a second sandbox project_ just to try out new features in development. For more details, refer to [Upgrade Core version in Cloud](/docs/dbt-versions/upgrade-dbt-version-in-cloud).
 
 We're leaving the rest of this post as is, so we can all remember how it used to be. Enjoy a stroll down memory lane.
 

@@ -12,6 +12,10 @@ date: 2024-04-22
 is_featured: true
 ---
 
+import Latest from '/snippets/_release-stages-from-versionless.md'
+
+<Latest/>
+
 dbt Cloud now includes a suite of new features that enable configuring precise and unique connections to data platforms at the environment and user level. These enable more sophisticated setups, like connecting a project to multiple warehouse accounts, first-class support for [staging environments](/docs/deploy/deploy-environments#staging-environment), and user-level [overrides for specific dbt versions](/docs/dbt-versions/upgrade-dbt-version-in-cloud#override-dbt-version). This gives dbt Cloud developers the features they need to tackle more complex tasks, like Write-Audit-Publish (WAP) workflows and safely testing dbt version upgrades. While you still configure a default connection at the project level and per-developer, you now have tools to get more advanced in a secure way. Soon, dbt Cloud will take this even further allowing multiple connections to be set globally and reused with _global connections_.
 
 <!--truncate-->
@@ -80,7 +84,7 @@ All you need to do is configure an environment as staging and enable the **Defer
 
 ## Upgrading on a curve
 
-Lastly, let’s consider a more specialized use case. Imagine we have a "tiger team" (consisting of a lone analytics engineer named Dave) tasked with upgrading from dbt version 1.6 to the new **Versionless** setting, to take advantage of added stability and feature access. We want to keep the rest of the data team being productive in dbt 1.6 for the time being, while enabling Dave to upgrade and do his work in the new versionless mode.
+Lastly, let’s consider a more specialized use case. Imagine we have a "tiger team" (consisting of a lone analytics engineer named Dave) tasked with upgrading from dbt version 1.6 to the new **Latest release track**, to take advantage of new features and performance improvements. We want to keep the rest of the data team being productive in dbt 1.6 for the time being, while enabling Dave to upgrade and do his work with Latest (and greatest) dbt.
 
 ### Development environment
 

@@ -1,5 +1,5 @@
 ---
-title: "How we're making sure you can confidently go \"Versionless\" in dbt Cloud"
+title: "How we're making sure you can confidently switch to the \"Latest\" release track in dbt Cloud"
 description: "Over the past 6 months, we've laid a stable foundation for continuously improving dbt."
 slug: latest-dbt-stability
 
@@ -12,23 +12,27 @@ date: 2024-05-02
 is_featured: true
 ---
 
+import Latest from '/snippets/_release-stages-from-versionless.md'
+
+<Latest/>
+
 As long as dbt Cloud has existed, it has required users to select a version of dbt Core to use under the hood in their jobs and environments. This made sense in the earliest days, when dbt Core minor versions often included breaking changes. It provided a clear way for everyone to know which version of the underlying runtime they were getting.
 
 However, this came at a cost. While bumping a project's dbt version *appeared* as simple as selecting from a dropdown, there was real effort required to test the compatibility of the new version against existing projects, package dependencies, and adapters. On the other hand, putting this off meant foregoing access to new features and bug fixes in dbt.
 
-But no more. Today, we're ready to announce the general availability of a new option in dbt Cloud: [**"Versionless."**](https://docs.getdbt.com/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless)
+But no more. Today, we're ready to announce the general availability of a new option in dbt Cloud: [**the "Latest" release track.**](/docs/dbt-versions/cloud-release-tracks)
 
 <!--truncate-->
 
 For customers, this means less maintenance overhead, faster access to bug fixes and features, and more time to focus on what matters most: building trusted data products. This will be our stable foundation for improvement and innovation in dbt Cloud. 
 
-But we wanted to go a step beyond just making this option available to you. In this blog post, we aim to shed a little light on the extensive work we've done to ensure that using "Versionless" is a stable, reliable experience for the thousands of customers who rely daily on dbt Cloud.
+But we wanted to go a step beyond just making this option available to you. In this blog post, we aim to shed a little light on the extensive work we've done to ensure that using the "Latest" release track is a stable and reliable experience for the thousands of customers who rely daily on dbt Cloud.
 
 ## How we safely deploy dbt upgrades to Cloud
 
 We've put in place a rigorous, best-in-class suite of tests and control mechanisms to ensure that all changes to dbt under the hood are fully vetted before they're deployed to customers of dbt Cloud.
 
-This pipeline has in fact been in place since January! It's how we've already been shipping continuous changes to the hundreds of customers who've selected "Versionless" while it's been in Beta and Preview. In that time, this process has enabled us to prevent multiple regressions before they were rolled out to any customers.
+This pipeline has in fact been in place since January! It's how we've already been shipping continuous changes to the hundreds of customers who've selected the "Latest" release track while it's been in Beta and Preview. In that time, this process has enabled us to prevent multiple regressions before they were rolled out to any customers.
 
 We're very confident in the robustness of this process**. We also know that we'll need to continue building trust with time.** We're sharing details about this work in the spirit of transparency and to build that trust.
 
@@ -82,9 +86,9 @@ All incidents are retrospected to make sure we not only identify and fix the roo
 
 :::
 
-The outcome of this process is that, when you select "Versionless" in dbt Cloud, the time between an improvement being made to dbt Core and you *safely* getting access to it in your projects is a matter of days — rather than months of waiting for the next dbt Core release, on top of any additional time it may have taken to actually carry out the upgrade.
+The outcome of this process is that, when you select the "Latest" release track in dbt Cloud, the time between an improvement being made to dbt Core and you *safely* getting access to it in your projects is a matter of days — rather than months of waiting for the next dbt Core release, on top of any additional time it may have taken to actually carry out the upgrade.
 
-We’re pleased to say that since the beta launch of “Versionless” in dbt Cloud in March, **we have not had any functional regressions reach customers**, while we’ve also been shipping multiple improvements to dbt functionality every day. This is a foundation that we aim to build on for the foreseeable future.
+We’re pleased to say that, at the time of writing (May 2, 2024), since the beta launch of the "Latest" release track in dbt Cloud in March, **we have not had any functional regressions reach customers**, while we’ve also been shipping multiple improvements to dbt functionality every day. This is a foundation that we aim to build on for the foreseeable future.
 
 ## Stability as a feature
 
@@ -98,7 +102,7 @@ The adapter interface — i.e. how dbt Core actually connects to a third-party d
 
 To solve that, we've released a new set of interfaces that are entirely independent of the `dbt-core` library: [`dbt-adapters==1.0.0`](https://github.com/dbt-labs/dbt-adapters). From now on, any changes to `dbt-adapters` will be backward and forward-compatible. This also decouples adapter maintenance from the regular release cadence of dbt Core — meaning maintainers get full control over when they ship implementations of new adapter-powered features.
 
-Note that adapters running in dbt Cloud **must** be [migrated to the new decoupled architecture](https://github.com/dbt-labs/dbt-adapters/discussions/87) as a baseline in order to support the new "Versionless" option.
+Note that adapters running in dbt Cloud **must** be [migrated to the new decoupled architecture](https://github.com/dbt-labs/dbt-adapters/discussions/87) as a baseline in order to support the new "Latest" release track.
 
 ### Managing behavior changes: stability as a feature
 
@@ -118,7 +122,7 @@ We’ve now [formalized our development best practices](https://github.com/dbt-l
 
 In conclusion, we’re putting a lot of new muscle behind our commitments to dbt Cloud customers, the dbt Community, and the broader ecosystem:
 
-- **Continuous updates**: "Versionless" dbt Cloud simplifies the update process, ensuring you always have the latest features and bug fixes without the maintenance overhead.
+- **Continuous updates**: The "Latest" release track in dbt Cloud simplifies the update process, ensuring you always have the latest features and bug fixes without the maintenance overhead.
 - **A rigorous new testing and deployment process**: Our new testing pipeline ensures that every update is carefully vetted against documented interfaces, Cloud-supported adapters, and popular packages before it reaches you. This process minimizes the risk of regressions — and has now been successful at entirely preventing them for hundreds of customers over multiple months.
 - **A commitment to stability**: We’ve reworked our approaches to adapter interfaces, behaviour change management, and metadata artifacts to give you more stability and control.
 

@@ -12,6 +12,10 @@ date: 2024-06-14
 is_featured: true
 ---
 
+import Latest from '/snippets/_release-stages-from-versionless.md'
+
+<Latest/>
+
 **New in dbt: allow Snowflake Python models to access the internet**
 
 With dbt 1.8, dbt released support for Snowflake’s [external access integrations](https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview) further enabling the use of dbt + AI to enrich your data. This allows querying of external APIs within dbt Python models, a functionality that was required for dbt Cloud customer, [EQT AB](https://eqtgroup.com/). Learn about why they needed it and how they helped build the feature and get it shipped!
@@ -114,6 +118,6 @@ Traditionally dbt is the T in ELT (dbt overview [here](https://docs.getdbt.com/t
 
 In order to get this functionality shipped quickly, EQT opened a pull request, Snowflake helped with some problems we had with CI and a member of dbt Labs helped write the tests and merge the code in!  
 
-dbt now features this functionality in dbt 1.8+ or the “Versionless” option of dbt Cloud (dbt overview [here](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless)). 
+dbt now features this functionality in dbt 1.8+ and the "Latest" release track in dbt Cloud (dbt overview [here](/docs/dbt-versions/cloud-release-tracks)). 
 
 dbt Labs staff and community members would love to chat more about it in the [#db-snowflake](https://getdbt.slack.com/archives/CJN7XRF1B) slack channel.
@@ -16,7 +16,7 @@
 exports.versions = [
   {
     version: "1.10",
-    customDisplay: "Cloud (Versionless)",
+    customDisplay: "Cloud (Latest)",
   },
   {
     version: "1.9",

@@ -8,7 +8,7 @@
 
 :::info Microbatch
 
-The new `microbatch` strategy is available in beta for [dbt Cloud Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) and dbt Core v1.9. 
+The new `microbatch` strategy is available in beta for [dbt Cloud "Latest"](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9. 
 
 If you use a custom microbatch macro, set a [distinct behavior flag](/reference/global-configs/behavior-changes#custom-microbatch-strategy) in your `dbt_project.yml` to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the [microbatch strategy](#how-microbatch-compares-to-other-incremental-strategies).
 

@@ -7,7 +7,7 @@
 ---
 <VersionBlock firstVersion="1.9">
 
-<!-- this whole section is for 1.9 and higher + Versionless -->
+<!-- this whole section is for 1.9 and higher + Release Tracks -->
 
 It's common in analytics engineering to have a date dimension or "time spine" table as a base table for different types of time-based joins and aggregations. The structure of this table is typically a base column of daily or hourly dates, with additional columns for other time grains, like fiscal quarters, defined based on the base column. You can join other tables to the time spine on the base column to calculate metrics like revenue at a point in time, or to aggregate to a specific time grain.
 
@@ -108,7 +108,7 @@
   - It needs to reference a column defined under the `columns` key, in this case, `date_hour` and `date_day`, respectively.
   - It sets the granularity at the column-level using the `granularity` key, in this case, `hour` and `day`, respectively. 
 - MetricFlow will use the `standard_granularity_column` as the join key when joining the time spine table to another source table.
-- [The `custom_granularities` field](#custom-calendar), (available in Versionless and dbt v1.9 and higher) lets you specify non-standard time periods like `fiscal_year` or `retail_month` that your organization may use.
+- [The `custom_granularities` field](#custom-calendar), (available in dbt Cloud Latest and dbt Core v1.9 and higher) lets you specify non-standard time periods like `fiscal_year` or `retail_month` that your organization may use.
 
 For an example project, refer to our [Jaffle shop](https://github.com/dbt-labs/jaffle-sl-template/blob/main/models/marts/_models.yml) example.
 
@@ -310,9 +310,7 @@
 
 <VersionBlock lastVersion="1.8">
 
-The ability to configure custom calendars, such as a fiscal calendar, is available in [dbt Cloud Versionless](/docs/dbt-versions/versionless-cloud) or dbt Core [v1.9 and higher](/docs/dbt-versions/core). 
-
-To access this feature, [upgrade to Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) or your dbt Core version to v1.9 or higher.
+The ability to configure custom calendars, such as a fiscal calendar, is available now in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks), and it will be available in [dbt Core v1.9+](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). 
 
 </VersionBlock>
 

@@ -95,7 +95,8 @@
 <VersionBlock lastVersion="1.8">
 Default time granularity for metrics is useful if your time dimension has a very fine grain, like second or hour, but you typically query metrics rolled up at a coarser grain. 
 
-To set the default time granularity for metrics, you need to be on dbt Cloud Versionless or dbt v1.9 and higher. 
+Default time granularity for metrics is available now in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks), and it will be available in [dbt Core v1.9+](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). 
+
 
 </VersionBlock>